In this paper, we present a simple yet effective pedestrian detector with a novel assignment strategy, achieving competitive accuracy while performing competitive inference time with the state-of-the-art methods. On top of a backbone, the proposed method can serve as a metric incorporated into the other pedestrian detectors, and experimental results show a consistent improvement on some popular pedestrian detection benchmarks, e.g. Citypersons and Caltech. This novel design is flexible and independent of any backbone network, without being limited by the two-stage detection framework. Therefore, it is also interesting to incorporate the proposed assignment strategy with other detectors like FCOS [1]} and YOLO [2]}, which will be studied in future. Furthermore, we also consider extending this method, not only for pedestrian detection, but also study the proposed method on more general object detection tasks.
